From the optimal weights, we can learn that the larger is the fraction \(\sigma ^{2}_{A}/\sigma ^{2}_{L}\)  and the smaller is the degree of the graph \(d\) , the more weight in the averaging will be given to the \(Q\) -function received from the learner. Similarly, when only a uniform average is possible to the agent, it is worthwhile to do so only if the degree \(d\)  is large enough compared to the noise parameters ratio. Although it is known that certain communication restrictions, such as collisions, become more dominant with increased connectivity [1]}, [2]}, we show here that for additive noise the opposite is true, and the performance improves with increased connectivity.
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